1. Field of the Invention
The present invention relates to an image enhancing method using mean-separate histogram equalization (MSHE) and a circuit therefor. More particularly, the present invention relates to an image enhancing method in which a given image is separated into a predetermined number of sub-images based on a mean level of the image and the respective sub-images are histogram-equalized independently, and a circuit therefor.
The instant application is based on Korean Patent Application Nos. 96-6219 and 96-11624 which are incorporated herein by reference for all purposes.
2. Description of the Related Art
A histogram of gray levels provides an overall description of the appearance of an image. Properly adjusted gray levels for a given image can enhance the appearance or contrast thereof.
Among the many methods for contrast enhancement, the most widely known one is histogram equalization, in which the contrast of a given image is enhanced according to the sample distribution thereof. Such a method is disclosed in the documents: 1! J. S. Lim, "Two-dimensional Signal and Image Processing," Prentice Hall, Englewood Cliffs, N.J., 1990, and 2! R. C. Gonzalez and P. Wints, "Digital Image Processing," Addison-Wesley, Reading, Mass., 1977.
Also, useful applications of the histogram equalization method for medical image processing and radar image processing are disclosed in the following documents: 3! J. Zimmerman, S. Pizer, E. Staab, E. Perry, W. McCartney and B. Brenton, "Evaluation of the Effectiveness of Adaptive Histogram Equalization for Contrast Enhancement," IEEE Tr. on Medical Imaging, pp. 304-312, December 1988, and 4! Y. Li, W. Wang and D. Y. Yu, "Application of Adaptive Histogram Equalization to X-ray Chest Image," Proc. of the SPIE, pp. 513-514, vol. 2321, 1994.
In general, since histogram equalization causes the dynamic range of an image to be stretched, the density distribution of the resultant image is made flat and the contrast of the image is enhanced as a consequence thereof.
However, this widely-known technique of histogram equalization can cause problems in some practical cases. That is, as the output density of the histogram equalization becomes uniform, the mean brightness of an output image approaches the middle gray level value. Actually, for histogram equalization of an analog image, the mean brightness of the output image is exactly the middle gray level regardless of the mean brightness of the input image. Obviously this feature is not desirable in an some real applications. For instance, an image taken at nighttime can appear to be an image taken in the daytime after histogram equalization has been performed. Meanwhile, image signals which are too dark or too bright result in low contrast after equalization.